Data-derived soft-sensors for biological wastewater treatment plants: An overview
This paper surveys and discusses the application of data-derived soft-sensing techniques in
biological wastewater treatment plants. Emphasis is given to an extensive overview of the …
biological wastewater treatment plants. Emphasis is given to an extensive overview of the …
Data-driven designs of fault detection systems via neural network-aided learning
With the aid of neural networks, this article develops two data-driven designs of fault
detection (FD) for dynamic systems. The first neural network is constructed for generating …
detection (FD) for dynamic systems. The first neural network is constructed for generating …
Computationally efficient model predictive control algorithms
M Ławryńczuk - A Neural Network Approach, Studies in Systems …, 2014 - Springer
In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …
of: the current control error (the proportional part), the past errors (the integral part) and the …
Support vector echo-state machine for chaotic time-series prediction
Z Shi, M Han - IEEE transactions on neural networks, 2007 - ieeexplore.ieee.org
A novel chaotic time-series prediction method based on support vector machines (SVMs)
and echo-state mechanisms is proposed. The basic idea is replacing" kernel trick" with" …
and echo-state mechanisms is proposed. The basic idea is replacing" kernel trick" with" …
[图书][B] Artificial neural networks for the modelling and fault diagnosis of technical processes
K Patan - 2008 - books.google.com
An unappealing characteristic of all real-world systems is the fact that they are vulnerable to
faults, malfunctions and, more generally, unexpected modes of-haviour. This explains why …
faults, malfunctions and, more generally, unexpected modes of-haviour. This explains why …
Learning to predict bus arrival time from heterogeneous measurements via recurrent neural network
J Pang, J Huang, Y Du, H Yu… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Bus arrival time prediction intends to improve the level of the services provided by
transportation agencies. Intuitively, many stochastic factors affect the predictability of the …
transportation agencies. Intuitively, many stochastic factors affect the predictability of the …
A sparse recurrent neural network for trajectory prediction of atlantic hurricanes
M Moradi Kordmahalleh, M Gorji Sefidmazgi… - Proceedings of the …, 2016 - dl.acm.org
Hurricanes constitute major natural disasters that lead to destruction and loss of lives.
Therefore, to reduce economic loss and to save human lives, an accurate forecast of …
Therefore, to reduce economic loss and to save human lives, an accurate forecast of …
[HTML][HTML] State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems
Reliable and timely fault detection and isolation are necessary tasks to guarantee
continuous performance in complex industrial systems, avoiding failure propagation in the …
continuous performance in complex industrial systems, avoiding failure propagation in the …
Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation
RK Al Seyab, Y Cao - Journal of Process Control, 2008 - Elsevier
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used
in nonlinear model predictive control (NMPC) context. The neural network represented in a …
in nonlinear model predictive control (NMPC) context. The neural network represented in a …
An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection
M Mrugalski - International Journal of Applied Mathematics and …, 2013 - sciendo.com
This paper presents an identification method of dynamic systems based on a group method
of data handling approach. In particular, a new structure of the dynamic multi-input multi …
of data handling approach. In particular, a new structure of the dynamic multi-input multi …